Gartner: CDAOs Now Lead Enterprise AI Strategy, Reordering C-Suite Power Toward Data-Driven Leadership

A quiet revolution is reshaping the C-suite as data leadership takes the driver’s seat in enterprise AI strategy. Recent Gartner findings reveal that Chief Data and Analytics Officers now lead AI initiatives in 70% of organizations, marking a significant shift away from traditional CIO and CTO dominance. As data volumes explode and AI moves from experimental projects to mission-critical investments, enterprises are rethinking who guides AI strategy, how data is governed, and how cross-functional teams translate technical potential into tangible business value. This emerging leadership model places data assets at the center of strategic decision-making and elevates the CDAO role from a data operations function to a core executive authority that shapes business outcomes across the organization. The evolution reflects a broader trend: leadership structures are reoriented around the strategic power of data, signaling that the future of competitive advantage increasingly depends on the ability to manage, leverage, and govern data with AI-enabled capabilities.

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The Emergence of the Chief Data and Analytics Officer

The ascent of the Chief Data and Analytics Officer is not a sudden occurrence but the culmination of a decades-long realignment in how organizations view technology leadership. Historically, CIOs were charged with infrastructure readiness, systems stability, and the reliability of IT operations, while CTOs focused on innovation pathways and technology development. In that traditional model, data assets were often treated as by-products of business processes rather than strategic assets with intrinsic value. But as data volumes grew exponentially and AI technologies matured from isolated experiments to enterprise-grade investments, the strategic calculus changed. Enterprises began to recognize that data—when properly governed, cleaned, and integrated—could unlock AI capabilities that deliver measurable business outcomes at scale. In this context, the Chief Data and Analytics Officer emerged as a leadership hybrid: an executive who understands both the data fabric that underpins AI initiatives and the business imperatives that those initiatives are meant to serve.

The transformation did not occur in a vacuum. It was driven by concrete evidence that data capabilities underpin AI success at the most senior levels. Gartner’s research, for instance, shows that 70% of CDAOs now own the primary responsibility for developing AI strategy and establishing operational frameworks that govern AI deployment across the enterprise. This shift signals a recognition that data assets—when aligned with analytics and AI capabilities—become the central engine for business value creation, not merely a backend function. The CDAO, therefore, sits at the confluence of data management, analytics, and AI strategy, translating data potential into institutional value through disciplined governance, scalable data architectures, and cross-functional collaboration. As data volumes and AI complexity continue to grow, the CDAO role has moved from a compliance and data quality focus to a broader mandate that encompasses strategic direction, implementation oversight, and the orchestration of enterprise AI programs.

This evolution reflects a broader redefinition of what constitutes leadership in enterprise technology. The data-centric model reframes data as the most valuable asset in an AI-powered economy, not as a by-product of transactions or operations. It is a shift in authority: data leadership is increasingly seen as central to strategic decision-making, with CDAOs guiding how data is sourced, stored, secured, and leveraged to generate business impact. The evidence of this shift extends beyond job titles and into reporting structures and governance practices. Gartner reports that 36% of CDAOs reported directly to the CEO in 2025, a notable rise from 21% in the prior year. This change in reporting lines illustrates that data leadership is no longer a peripheral function but a strategic cornerstone of executive leadership and enterprise-wide decision-making. When data leadership anchors AI strategy at the highest levels of the organization, the potential to influence strategic outcomes—ranging from product development to risk management and customer experience—becomes more pronounced.

The transformation also reflects a fundamental change in how organizations conceive AI readiness. CDAOs now bring not only technical fluency but cross-functional visibility that enables them to translate AI potential into business value. The combination of data expertise, governance oversight, and broad organizational reach empowers CDAOs to steer AI initiatives from ideation to execution, ensuring that AI investments are aligned with business priorities and are capable of delivering measurable returns. As Gartner’s analysts observe, the 2025 era marks a critical point: AI leadership is no longer a narrow technical concern but a strategic capability that requires CDAOs to operate with executive-level influence and accountability. In short, CDAOs have become the linchpin of enterprise AI, bridging gaps between data assets, governance frameworks, and practical business applications across diverse functions.

Within organizations, this shift has multifaceted implications. For CDAOs, the expanded mandate means flattening silos and fostering collaboration across IT, data science, product, operations, finance, and marketing. It means building a shared language around data quality, data lineage, and AI outcomes so that non-technical executives can engage meaningfully with data-driven programs. For the broader executive team, it means rethinking the governance model for AI, balancing speed with risk management, and ensuring that AI initiatives are designed to deliver durable impact rather than isolated wins. The rising prominence of the CDAO also places new emphasis on governance, ethics, and risk management. As AI initiatives proliferate, CDAOs must implement governance frameworks that address data privacy, security, bias, and accountability. In this environment, the CDAO is not merely a data steward but a strategic navigator who aligns technical capabilities with business strategy, risk tolerance, and stakeholder expectations.

Gartner’s evidence of this evolving landscape extends to organizational reporting and the strategic influence that CDAOs command. The data leadership function has moved from a support role to a central driver of corporate strategy, with CDAOs occupying a position that shapes the direction of AI programs and the prioritization of data-related investments. The growing direct reporting relationship to CEOs underscores the strategic weight CDAOs now carry. It signals a shift toward executive accountability for data governance and AI outcomes, reinforcing the view that data capabilities are essential to maintaining competitive advantage in a data-driven economy. The implications for CIOs and CTOs are equally important. As CDAOs assume greater strategic influence, CIOs and CTOs must recalibrate their own roles to complement data leadership by focusing on enabling platforms, scalable data infrastructure, and enterprise-wide data stewardship in tandem with CDAOs. The rebalancing of authority within technology leadership roles is, therefore, a natural outcome of the push to unlock AI value through robust data governance and cross-functional collaboration.

Sarah James, a Senior Director Analyst at Gartner, highlights the transitional nature of 2025 for CDAOs. She characterizes this year as critical because AI presents a new opportunity for CDAOs to solidify their standing within AI leadership. James emphasizes that the CDAO’s exposure across the organization, combined with their expertise in data that is ready for AI applications, positions them uniquely to lead, guide, and challenge their organizations to deliver AI-driven value. This perspective aligns with the broader narrative of CDAOs as transformation agents who can translate technical capabilities into strategic, measurable outcomes. The increasing visibility of CDAOs at the executive level—coupled with their responsibility for building AI-enabled data foundations—suggests that organizations view data leadership as essential to sustaining AI momentum, rather than as a discrete, isolated area of focus. The 2025 milestone thus serves not only as a benchmark for the CDAO function but as a signal of a broader reevaluation of how data, analytics, and AI are governed at the highest levels of corporate leadership.

Looking ahead, the trajectory of the CDAO role is shaped by both opportunity and risk. On the opportunity side, the CDAO is well-positioned to lead enterprise AI efforts by aligning data assets with strategic business goals, coordinating cross-functional teams, and advocating for responsible, ethical AI practices. On the risk side, CDAOs face the challenge of proving impact in a landscape where AI investments are scrutinized by boards and executives seeking tangible returns. Gartner’s projection for 2027—that 75% of CDAOs who are not perceived as essential to AI implementation success will lose their C-level status—highlights the high-stakes nature of the role. This projection underscores the critical need for CDAOs to not only oversee data governance and AI strategy but to demonstrate clear, measurable business value through AI-enabled outcomes. The path to maintaining executive relevance, therefore, involves continuously strengthening the strategic relevance of the CDAO function through visible, data-driven results, effective stakeholder management, and successful governance of AI initiatives across the enterprise.

In sum, the emergence of the CDAO signals a fundamental rethinking of how organizations lead and govern AI in practice. The role evolves beyond data management into strategic leadership that links data assets to business outcomes, governance, and enterprise-wide AI execution. As AI becomes a central driver of competitive differentiation, CDAOs are increasingly recognized as the custodians of data-driven strategy—responsible for shaping AI roadmaps, ensuring data quality and governance, and driving measurable value across the enterprise. The trajectory suggests that CDAOs will continue to play a pivotal role in the C-suite, guiding AI strategy, overseeing data governance, and coordinating cross-functional efforts to realize the full potential of AI in a data-centric business environment.

Structural Reorganization: Reporting Lines and Organizational Impact

The governance and organizational placement of the Chief Data and Analytics Officer is a critical determinant of how effectively data and AI strategies are translated into business value. The Gartner findings illuminate a visible shift in executive reporting lines, with a growing share of CDAOs reporting directly to the CEO—36% in 2025, up from 21% the previous year. This rising trend in direct CEO reporting signals a broader recognition that data leadership has transcended its traditional “back-office” or IT-aligned roles and has become a strategic function that informs decisions at the highest level of the organization. Direct CEO access enables CDAOs to influence strategic priorities, secure funding for data and AI initiatives, and ensure alignment between data governance, risk management, and business outcomes. By positioning data leadership at the apex of the organizational chart, enterprises can better manage cross-functional dependencies and coordinate AI initiatives with enterprise strategy, product roadmaps, and customer-centric objectives.

The shift in reporting structures does more than alter lines of authority; it reshapes how decisions are made and how accountability is distributed across the organization. When CDAOs report to the CEO, they gain visibility into the most critical strategic conversations, including portfolio prioritization, risk considerations, and performance measurement of AI programs. This elevated position enhances the CDAOs’ ability to advocate for data governance investments, define data standards, and enforce a coherent data strategy across disparate business units. It also places a premium on the ability to communicate the business value of data initiatives to non-technical audiences, a capability that requires strong storytelling, business acumen, and strategic thinking. In practice, this means CDAOs must be able to present a compelling, data-driven case for AI investments, articulate how data governance aligns with enterprise risk appetite, and translate technical metrics into business outcomes that matter to boards and executives.

The organizational implications extend to how budgets, resources, and governance responsibilities are allocated. With the CDAO in a direct line of sight to the CEO, data and analytics budgets can be framed as strategic investments rather than operational expenditures. This enhances the ability to secure funding for data infrastructure, data quality initiatives, data integration projects, and AI platforms. Moreover, this shift frequently correlates with the creation or expansion of cross-functional data governance bodies, such as enterprise data councils or AI governance boards, designed to coordinate policy, standards, and risk management across the organization. When CDAOs sit at the center of strategic coordination, they enable more consistent data practices across business units, reduce duplication of data assets, and improve the reliability and speed of AI-enabled decision-making.

However, moving CDAOs into higher chairs also introduces new challenges. The increased visibility and accountability demand stronger political navigation, broader stakeholder management, and a deeper understanding of how AI investments translate into enterprise value. CDAOs must articulate a clear roadmap that links data strategy with business strategy and demonstrate measurable progress toward AI-enabled outcomes. They must also collaborate effectively with other executive roles—such as the CIO, CTO, Chief Data Steward, Chief Privacy Officer, and Chief Risk Officer—to avoid conflicts of authority and ensure a coherent governance framework. The interplay among these roles is crucial: while the CDAO may own AI strategy and data governance, CIOs are often responsible for the underlying digital platforms, data infrastructure, and security, making alignment essential to avoid fragmentation or conflicting priorities.

From a human capital perspective, the structural reorganization has implications for career development and talent management within technology leadership. As CDAOs take on more strategic responsibilities, there is growing emphasis on building capabilities in governance, policy development, risk management, and executive communication. CDAOs must cultivate a leadership presence that resonates with board members, senior executives, and line-of-business leaders. They must also foster a culture of data-driven decision-making that permeates the organization, ensuring that data ethics, privacy, and security considerations are integrated into AI initiatives from inception to deployment. In this context, the CDAO’s role becomes a catalyst for organizational learning—driving the adoption of data literacy, the adoption of standardized data practices, and the maturation of AI governance processes across all business units.

Gartner’s data on reporting lines and strategic positioning underscores the broader trend of data-led leadership becoming a central axis around which enterprise AI strategy revolves. The movement toward CEO-level visibility and accountability reflects a recognition that data governance, data quality, and AI outcomes have a direct and meaningful impact on core business performance. As organizations continue to navigate the complexities of AI adoption, the structural alignment that places the CDAO within direct reach of the CEO will likely persist and expand, further reinforcing the strategic importance of data leadership in shaping enterprise strategy, risk management, and the competitive landscape. In practical terms, executives should anticipate continued investment in cross-functional governance mechanisms, enhanced collaboration between data management and AI development teams, and a sustained emphasis on translating data-driven insights into strategic advantage that manifests in revenue growth, operational efficiency, and customer value.

A broader implication of this structural shift is the potential for reformulation of performance metrics around data and AI initiatives. When CDAOs operate within a CEO-accessible framework, success criteria can be defined in terms of business outcomes such as revenue impact from AI-enabled products, reductions in operational risk due to improved data governance, improvements in customer experience resulting from data-driven personalization, and speed-to-value in AI program delivery. This outcome-focused measurement approach helps ensure that data initiatives remain tightly aligned with strategic priorities, enabling the organization to track progress in a rigorous, transparent manner. It also provides a framework for accountability, enabling better pacing of AI investments and more effective governance of risk and compliance concerns. Ultimately, the organizational shift toward CEO-level governance for CDAOs reinforces the centrality of data and analytics in corporate strategy and ensures that AI initiatives are anchored in a coherent, comprehensive, and measurable plan that resonates with executive leadership and the broader organization.

In this evolving landscape, those who lead data strategy must cultivate the ability to navigate complex organizational dynamics, align diverse stakeholders around shared objectives, and drive a data-centric culture that supports scalable AI transformation. As CDAOs assume more prominent roles, organizations will increasingly rely on them to integrate governance, ethics, data quality, and AI capabilities into a single, coherent strategic framework. This approach helps ensure that AI investments deliver durable value and that organizations remain resilient in the face of rapid technological change. The structural reorganization, therefore, is not merely a reshuffling of reporting lines; it is a strategic enabler of enterprise AI that strengthens governance, fosters cross-functional collaboration, and elevates the strategic influence of data leadership across the organization.

The Three Evolution Paths for CDAs

Gartner has identified three distinct trajectory paths for Chief Data and Analytics Officers based on its study of how data leadership evolves within the enterprise. Each path represents a different emphasis, set of responsibilities, and route to achieving credibility and impact in AI initiatives. While the paths are unique, they share a common objective: to transform data and analytics capabilities into tangible business value through effective leadership, governance, and cross-functional collaboration. Understanding these paths helps organizations tailor career development, align expectations, and design governance and transformation programs that best support their strategic goals. The three evolution paths are the Expert Data and Analytics Leader, the Connector, and the Pioneer CDAx. Each path reflects a different approach to combining data stewardship, analytics execution, and AI strategy within a broader enterprise context.

The Expert Data and Analytics Leader

The Expert Data and Analytics Leader path positions the CDAO as the central authority on data matters, with a primary focus on how data is collected, stored, managed, and leveraged to support business intelligence and analytics initiatives. In this path, the CDAO is the guardian of data integrity and consistency across the enterprise, providing a unified, reliable view of master data and ensuring that analytics systems rely on a single source of truth. This role typically reports to the IT department, reinforcing the traditional alignment of data infrastructure and governance with technology platforms and operational reliability. The Expert Data and Analytics Leader oversees the organization’s data governance program, master data management, data quality initiatives, and the governance frameworks that define how data assets are curated and used. The executive’s core responsibility is to ensure data integrity, availability, and accessibility for analytics applications, dashboards, reporting systems, and AI projects that rely on high-quality data inputs.

In practice, this path emphasizes establishing and maintaining enterprise-wide data standards, definitions, and policies. The Expert Data and Analytics Leader ensures consistent data usage across business units, aligning data governance with strategic analytics and AI requirements. The role involves a robust collaboration with data stewards, data engineers, data scientists, and business analysts to guarantee that data assets support accurate, timely, and meaningful insights. The success metrics for this trajectory focus on data quality improvements, data lineage clarity, reduction of data silos, and the reliability of data feeds used by analytics platforms and AI pipelines. The role may also encompass overseeing data integration programs, data cataloging efforts, and the design of scalable data architectures that support advanced analytics.

The benefits of this path include strong governance discipline, clear data lineage, and a stable foundation for analytics and AI to operate upon. The CDAO in this path can serve as a trusted advisor to the CEO and other executives by delivering high-confidence data-driven insights and reliable analytics outputs. However, this path may face limitations in terms of cross-functional influence if the CDAO’s responsibilities remain primarily within the IT or data management domain. To maximize impact, CDAOs pursuing this path should actively cultivate cross-functional collaboration, expand their influence into business-facing analytics initiatives, and work to demonstrate how robust data governance translates into strategic business outcomes beyond engineering and operations.

The Connector

The Connector path reimagines the CDAO as the bridge between the C-suite and the technical teams responsible for data management and AI development. This role emphasizes embedding analytics capabilities into products and services while advancing the AI agenda throughout the organization. The Connector operates at the interface where business needs, product strategy, and technical execution meet. The main objective is to ensure that analytics-driven insights inform decision-making across product development, customer experience, and operational optimization, and to drive the adoption of AI capabilities across the organization. The CDAO in this path is deeply involved in translating business requirements into data and analytics specifications, working closely with product managers, engineers, data scientists, and line-of-business leaders to embed analytics into the organizational fabric.

A key aspect of the Connector path is the emphasis on cross-functional collaboration and communication. The CDAO acts as a translator who can articulate the value and feasibility of data and AI initiatives in business terms, helping non-technical stakeholders grasp how data assets enable competitive differentiation. This path also entails ensuring that analytics capabilities align with the company’s strategic priorities while managing expectations across diverse stakeholders. The CDAO’s governance responsibilities include setting standards for analytics governance, responsible AI practices, and ensuring that AI-enabled products and services comply with ethical and legal requirements. The success metrics in this trajectory center on the adoption rate of analytics-powered features, the speed with which data-driven insights inform product decisions, and the breadth of AI utilization across business units.

The Connector path’s strength lies in its ability to catalyze widespread data-driven decision-making by lowering barriers to analytics adoption. It requires strong storytelling, stakeholder management, and the capacity to translate technical feasibility into practical business value. CDAOs following this path must balance credibility with approachability, maintaining clear communication channels with both executives and technical teams and ensuring alignment between business goals and analytics execution. This path is well-suited for organizations prioritizing rapid AI-enabled product innovation, fast data-driven experimentation, and the scaling of analytics across multiple lines of business.

The Pioneer CDAx

The Pioneer CDAx path represents the most expansive and transformative approach among the three. In this trajectory, the CDAO serves as a transformation agent who leads the fusion of data, analytics, and AI leadership responsibilities in a way that drives organizational change. The Pioneer CDAx is a cross-functional protagonist who champions ethical principles, governance frameworks, and governance-driven innovation across the enterprise. This role integrates data management, analytics, and AI leadership with a focus on organizational transformation, technology adoption, and culture change. The Pioneer CDAx is expected to foster cross-functional collaboration, break down silos, and democratize data and AI capabilities across the organization.

The responsibilities of the Pioneer CDAx extend beyond operational governance and analytics execution to include strategic transformation efforts. This includes developing and promoting ethical AI frameworks, ensuring accountability for AI systems, and instituting governance mechanisms that address risk, bias, privacy, and security in AI deployments. The Pioneer CDAx leads initiatives that align data and AI initiatives with broader business transformation goals, such as customer-centric innovation, process optimization, and new business models. The role emphasizes governance, change management, and collaboration across functions to enable cross-pollination of ideas, skills, and technologies that accelerate AI-enabled transformation.

To succeed on the Pioneer CDAx path, the CDAO must cultivate a diverse set of capabilities that span business acumen, strategic leadership, technical depth, and change management expertise. They must articulate the value of data and AI in terms that resonate with business leaders, stakeholders, and customers. They must build AI-ready data foundations that ensure quality, reliability, and scalability while fostering a culture of ethical and responsible AI development. The Pioneer CDAx must also champion governance that ensures safe, compliant, and transparent AI systems across the enterprise. Collaboration across silos—such as product, engineering, risk, privacy, and legal—becomes central to this path, enabling cross-functional teams to innovate while maintaining governance discipline.

Each of these three paths requires different skill sets and leadership styles, and Gartner emphasizes that D&A leadership will continue to diverge in the near future. For existing CDAOs or aspiring ones, the takeaway is clear: develop the skills and expertise that align with the chosen path, and build the organizational relationships that empower you to fulfill that path effectively. Sarah James notes that no other role has the opportunity to occupy the central nexus of the D&A and AI coalition as the CDAO does, highlighting the unique leverage that CDAOs can achieve when they position themselves at the heart of data strategy and enterprise AI delivery. The evolving ecosystem thus invites CDAOs to select a path that aligns with their strengths and the organization’s needs, while remaining adaptable to shifting priorities as AI initiatives mature and scale across the enterprise.

Pathway Alignment and Talent Development Implications

The three trajectories—Expert Data and Analytics Leader, Connector, and Pioneer CDAx—do not exist in isolation; rather, they reflect a continuum of capabilities that organizations may emphasize at different stages of AI maturity. A mature enterprise may combine elements of all three paths, allowing CDAOs to transition across roles as business priorities and AI investments evolve. This flexibility is critical because AI programs often begin with data governance and analytics foundations, then move toward broader AI productization and, eventually, organization-wide transformational initiatives. In such contexts, CDAOs may start on a path that emphasizes data integrity and governance (Expert Data and Analytics Leader), gradually expand to become the cross-functional communicator and multiplier of analytics capabilities (Connector), and eventually assume the visionary role of driving enterprise-wide transformation through data, analytics, and AI (Pioneer CDAx).

To optimize outcomes, organizations should design talent development programs that prepare CDAOs for this evolution. This includes building robust governance frameworks that are adaptable to changing AI landscapes, fostering cross-functional collaboration skills, and investing in leadership development that strengthens credibility with the board and other senior executives. Organizations should also ensure that CDAOs have access to the right mix of data, analytics tools, and AI platforms to execute their chosen path effectively. Continuous assessment of AI maturity and data readiness can help determine which path is most appropriate at different stages of the organization’s AI journey, and what capabilities need to be developed or enhanced to achieve desired outcomes. By embracing the diversity of these paths and aligning talent development with strategic objectives, enterprises can cultivate CDAOs who are not only technically proficient but also capable of driving strategic value through data and AI leadership.

In summary, Gartner’s three evolution paths for CDAOs provide a framework for understanding how data leadership can mature within organizations. The Expert Data and Analytics Leader, the Connector, and the Pioneer CDAx each offer distinct advantages and challenges, and organizations can tailor their leadership development and governance strategies accordingly. This framework highlights the importance of aligning the CDAO’s role with the organization’s AI maturity, business goals, and risk tolerance, while ensuring that data governance, ethics, and cross-functional collaboration remain central to AI strategy. As the enterprise AI landscape broadens and deepens, CDAOs who can navigate these paths with clarity, credibility, and strategic impact will be best positioned to shape the future of AI-driven business.

The Ecosystem of Skills, Capabilities, and Transformation Playbooks

The rise of the CDAO as the central orchestrator of data and AI strategy requires a comprehensive set of capabilities that span technology, governance, business acumen, and leadership. The modern CDAO must be able to translate complex data science and AI concepts into business value, communicate effectively with non-technical stakeholders, and drive cross-functional collaboration that accelerates AI adoption while maintaining ethical standards and risk controls. The following areas capture the core skill sets and organizational capabilities that CDAOs need to cultivate to succeed in this new leadership paradigm.

First, strategic business acumen and communication. CDAOs must understand business strategy, market dynamics, and the competitive landscape to shape AI priorities that deliver measurable business outcomes. This requires the ability to connect data assets and analytics capabilities to revenue growth, improved customer experience, and operational efficiency. It also demands the skill to present complex data-driven insights in a way that resonates with executives, boards, and business leaders who may not have a technical background. Clear storytelling, precise framing of hypotheses, and the ability to articulate trade-offs between risk, cost, and impact are critical. The CDAO’s communication should translate data governance decisions and AI strategies into concrete business cases, dashboardable metrics, and performance indicators that demonstrate value and guide decisions at the executive level.

Second, governance, risk, and ethics. Data governance is the backbone of credible AI initiatives. CDAOs must design and enforce governance frameworks that address data quality, data lineage, access controls, privacy, security, bias mitigation, and accountability. They should champion ethical AI practices and ensure that AI systems operate in a transparent and auditable manner. This includes establishing policies for data usage, model validation, and monitoring, as well as governance mechanisms that enable rapid response to governance concerns. The ability to balance innovation with risk management is essential, especially as AI projects scale and touch several parts of the organization. CDAOs must also align governance with regulatory requirements, reporting obligations, and enterprise risk management frameworks.

Third, data foundations and technical excellence. The technical side of the CDAO’s remit encompasses data architecture, data quality, data integration, metadata management, and data cataloging. Building AI-ready data foundations means ensuring data is clean, consistent, timely, and trustworthy for AI algorithms and analytics applications. CDAOs should oversee data platforms, data pipelines, and data stewardship programs that enable reliable data flows, supporting everything from business intelligence to advanced AI modeling. They must facilitate interoperability across diverse data sources and systems, ensuring that data assets can be integrated into AI pipelines with minimal friction and maximal reliability. This requires collaboration with data engineers, data scientists, and IT teams to design scalable data ecosystems that can support evolving AI workloads.

Fourth, cross-functional collaboration and change management. Effective CDAOs can break down silos and foster a culture of data-driven decision-making across the organization. They must build relationships with leaders from product, marketing, sales, operations, finance, and legal to align data and AI initiatives with business priorities. Change management skills are crucial to drive adoption of data practices and AI capabilities, including training, governance policy adoption, and the alignment of incentives with data-driven outcomes. The CDAO should champion a culture of continuous learning and experimentation, while maintaining governance controls that ensure responsible AI development and deployment.

Fifth, analytics and AI execution. The CDAO must oversee the end-to-end analytics lifecycle, from problem framing and data preparation to model development, evaluation, deployment, and monitoring. This includes selecting appropriate analytics methodologies, defining success criteria, and measuring outcomes in terms of business impact. The ability to scale analytics and AI across the organization is a critical capability. The CDAO should oversee program management for AI initiatives, ensuring consistent delivery, alignment with strategy, and the capacity to adapt to changing business needs and regulatory constraints. The role requires a strong partnership with data scientists, engineers, product teams, and business leaders to translate insights into decisions that drive value.

Sixth, measurement, ROI, and governance continuity. The CDAO must establish robust metrics and reporting mechanisms to demonstrate return on investment for data and AI initiatives. This includes defining key performance indicators (KPIs) for data quality, AI adoption, time-to-value, and business impact. The leadership must ensure governance continuity across transformations, maintaining momentum even as organizational priorities shift. This requires the ability to program governance into ongoing operations, creating a durable framework that can adapt to new AI deployments, evolving data landscapes, and emerging risks.

Seventh, leadership and organizational culture. The CDAO’s leadership style must align with the organization’s culture and strategic objectives. This entails building trust with peers and subordinates, inspiring a shared vision for data and AI, and guiding teams through the challenges of large-scale transformation. The CDAO should cultivate talent, attract diverse perspectives, and promote an inclusive environment that fosters collaboration and innovation. The leadership role extends beyond technical competence to include the strategic mentorship and development of cross-functional teams that will sustain AI initiatives over time.

These competencies are not merely aspirational; they are essential to achieving the strategic outcomes that organizations seek from AI investments. Gartner’s framework suggests that CDAOs will need to continuously expand their capabilities across these domains to maintain relevance and drive sustained value in the AI-enabled enterprise. As AI initiatives proliferate and scale, the CDAO’s ability to balance governance with speed, risk with opportunity, and data quality with innovation will determine the success of the organization’s AI strategy. The most effective CDAOs will be those who can articulate a compelling business case for data and AI, build strong governance foundations, and lead cross-functional teams toward a shared vision of data-driven, AI-enabled value creation.

In practice, organizations can operationalize this skill and capability framework through a combination of governance design, talent development, and program execution strategies. Governance design involves establishing a scalable data governance model, creating clear roles and responsibilities, and implementing policies and procedures that can manage data assets throughout their lifecycle. Talent development focuses on cultivating the capabilities described above, including leadership development, cross-functional training, and the recruitment of specialists who can contribute to the data and AI agenda. Program execution strategies emphasize the disciplined deployment of AI initiatives, from pilot programs to large-scale rollouts, with a clear alignment to business outcomes and a rigorous measurement framework. By integrating governance, talent, and program execution, organizations can create a robust, end-to-end approach that supports the CDAO-led AI strategy and sustains impact over time.

The practical takeaway for practitioners is that a successful CDAO-led AI strategy requires more than technical prowess. It demands a holistic leadership approach that integrates governance, data foundations, business acumen, and change management. CDAOs must be able to translate data and AI concepts into business language, cultivate cross-functional alliances, and steward ethical, responsible AI practices. They must also be prepared to navigate organizational complexity, manage expectations, and demonstrate continued value through measurable outcomes. This is the essence of the CDAO leadership model in the AI era: a role that sits at the center of data strategy, analytics execution, and AI transformation, driving business value through disciplined governance, robust data foundations, and cross-functional collaboration.

Readiness, Governance, and the Path to Measurable AI Value

Across industries, leaders are increasingly asking: how do we ensure that AI investments deliver tangible business value, and how can data governance and AI governance work together to accelerate outcomes without compromising ethics or compliance? Gartner’s research provides a practical lens on how CDAOs can build readiness and governance structures that support scalable, responsible AI. The core premise is that enterprise AI success hinges on data readiness—the quality, accessibility, and compatibility of data across the organization—and on governance frameworks that enable consistent, ethical, and secure use of AI technologies. Readiness encompasses data quality, documentation, lineage, and the ability to harmonize disparate data sources so that AI models can be trained, validated, deployed, and monitored in a reliable manner. It also means ensuring the data and analytics teams have access to the right tools, platforms, and talent to execute AI initiatives effectively.

A key implication of this readiness-driven approach is the centrality of governance. Data governance and AI governance must work in concert rather than as separate strands. Data governance ensures that data assets are well-managed, curated, and protected, while AI governance ensures that AI models are developed and deployed with oversight, accountability, and transparency. Together, these governance disciplines create a safety net that mitigates risk, promotes compliance, and supports responsible AI innovation. The CDAO plays a pivotal role in integrating these governance streams, establishing policies and controls that guide how data is collected, stored, accessed, used, and governed in AI systems. They also drive alignment with regulatory obligations, privacy considerations, and ethical standards, balancing the need for innovation with the imperative to protect stakeholders and maintain public trust.

Organizations pursuing a CDAO-led AI strategy should consider several practical steps to operationalize readiness and governance. First, they should conduct a comprehensive data asset inventory to identify critical data domains, data sources, and data quality gaps. This inventory informs data governance policies and helps prioritize investments in data quality improvements, data integration, and metadata management. Second, they should design and implement an enterprise data governance framework that defines roles, responsibilities, decision rights, and escalation paths. This framework should also establish data standards, data lineage practices, and data access controls to ensure consistent governance across the organization. Third, they should design AI governance processes that cover model development, validation, deployment, monitoring, and ongoing risk assessment. This includes setting up ethics review boards, bias detection protocols, and model audit requirements to maintain accountability and transparency in AI systems.

The CDAO, working in collaboration with the CIO, CTO, Chief Privacy Officer, and other senior leaders, can champion a holistic governance approach that integrates data governance with AI governance. This approach ensures that AI initiatives are guided by a unified policy framework and are aligned with the organization’s risk appetite, compliance requirements, and strategic priorities. It also provides the structure needed to scale AI across the enterprise while maintaining control over data quality, model risk, and ethical considerations. The governance framework, when implemented effectively, reduces the likelihood of data quality issues bottlenecking AI deployment and helps ensure consistent, auditable outcomes across business units.

In practice, governance and readiness translate into measurable improvements in AI delivery. When data assets are clean, well-documented, and readily accessible, AI models can be trained faster and with greater reliability. When governance controls are in place, AI deployments are more likely to meet regulatory requirements, maintain ethical standards, and deliver consistent performance across varying contexts. This, in turn, increases confidence among executives, boards, and customers that AI initiatives are being managed with rigor and accountability. The CDAO’s leadership is central to this process, as they coordinate across functional boundaries to implement governance, manage risk, and deliver business value through AI-enabled capabilities.

As AI initiatives continue to expand, CDAOs will be measured on their ability to translate governance and readiness into value. The success metrics will likely include improvements in data quality scores, reductions in data-related safety and compliance incidents, faster AI time-to-value from concept to deployment, and demonstrable business outcomes attributable to AI-enabled decisions. The governance framework’s effectiveness will be judged by its ability to support consistent, scalable, and ethical AI deployments across the organization. The CDAO’s role as the governance integrator, data stewards, and cross-functional advocate for data-driven AI strategy positions them to drive continuous improvement and sustained value, while ensuring that governance remains rigorous and adaptive to evolving AI technologies and regulatory landscapes.

Practical Roadmap: From Data Strategy to Enterprise AI Value

To convert the strategic insights about CDAOs, governance, and AI readiness into concrete, actionable outcomes, organizations can adopt a practical roadmap that guides the design, implementation, and maturation of CDAO-led AI strategy. This roadmap is not a rigid blueprint but a flexible framework that can be tailored to an organization’s industry, size, data maturity, and AI ambitions. The following steps outline a holistic approach to establishing data-led AI leadership that delivers measurable value.

Step 1: Define AI strategy aligned with business goals. The first step involves articulating how AI will create value across the enterprise, including product innovation, customer experience, operations optimization, and risk reduction. The strategy should specify scope, priorities, expected outcomes, and success criteria. The CDAO, in collaboration with the CEO and other senior leaders, will define the strategic objectives for data and AI initiatives, identifying the data assets and analytics capabilities required to achieve those objectives. The strategy must also consider governance, risk, and compliance requirements to ensure responsible AI adoption from the outset.
Step 2: Inventory and assess data assets. Conduct a comprehensive data asset inventory to map data sources, data quality, data lineage, and data ownership. The assessment should identify data gaps, data silos, and data quality issues that could hinder AI performance. The CDAO and data governance teams should prioritize remediation efforts, establish data quality metrics, and create a data catalog to enable discoverability and reuse. This step lays the foundation for AI readiness by ensuring the organization has access to the right data assets in a reliable, scalable, and secure manner.
Step 3: Build AI-ready data foundations. Invest in data architecture, data integration, metadata management, and data quality tooling that enable AI workflows. Create standardized data models and data definitions to ensure consistent interpretation and usage of data across business units. Establish data pipelines that provide timely, accurate, and accessible data for analytics and AI workloads. This step requires close collaboration between the CDAO, CIO, data engineers, data scientists, and product teams to design and implement data platforms that support scalable AI initiatives.
Step 4: Establish governance for data and AI. Implement an integrated governance framework that covers data governance (quality, lineage, access, privacy) and AI governance (model validation, monitoring, bias mitigation, accountability). Define roles and responsibilities, decision rights, and escalation paths. Set up governance bodies and working groups that bring together stakeholders from across the organization to oversee data and AI initiatives, ensure compliance, and manage risk. This governance foundation should be designed to scale with the organization as AI initiatives proliferate.
Step 5: Embed analytics into the product and service repertoire. The Connector path emphasizes embedding analytics capabilities into products and services. In practice, this means identifying product areas where data-driven insights can create competitive differentiation, integrating analytics into product roadmaps, and enabling AI-enabled features that deliver customer value. The CDAO coordinates with product leaders and engineering teams to translate business requirements into analytics specifications, ensuring alignment with governance policies and ethical standards.
Step 6: Scale AI responsibly and ethically. As AI projects move from pilots to enterprise-wide deployments, the organization must implement processes for ethical AI, model risk management, and ongoing monitoring. The CDAO champions responsible AI practices, ensuring models are auditable, explainable, and tested for biases. This step includes establishing monitoring dashboards, incident response plans, and governance controls that adapt to new AI use cases and evolving regulatory landscapes.
Step 7: Measure outcomes and communicate value. Define and track KPIs that capture the business impact of data and AI initiatives. Metrics might include data quality improvements, time-to-value for AI projects, adoption rates of analytics features, and financial or operational outcomes attributable to AI deployments. The CDAO should deliver regular updates to executives and boards that translate data governance and AI performance into business value, reinforcing the strategic role of data leadership.
Step 8: Foster a data-driven culture and capability building. Invest in ongoing education and capability development to increase data literacy across the organization. This includes training programs for business users, data science teams, and product developers, promoting a shared language around data and AI, and creating communities of practice that accelerate knowledge sharing. A data-driven culture enhances the adoption of analytics and AI and sustains momentum as new use cases emerge.
Step 9: Evolve leadership and governance as AI matures. As AI matures within the organization, the CDAO’s role may evolve to reflect new priorities and emerging capabilities. This could involve expanding cross-functional governance, refining ethical standards, and scaling governance to accommodate more complex AI systems. The organization should remain adaptable, ensuring that governance, data foundations, and leadership remain aligned with strategic objectives and market dynamics.
Step 10: Maintain executive alignment and accountability. Regular executive-level reviews ensure that data and AI initiatives stay aligned with business goals, budgets, and risk tolerance. The CDAO, along with the CEO and other senior leaders, should review progress, adjust priorities as needed, and reaffirm commitments to responsible AI and data governance. This ongoing alignment sustains momentum and ensures continued value from data and AI investments.

Implementing this practical roadmap requires disciplined program management, stakeholder engagement, and a long-term view of AI maturity. It also requires a willingness to adjust the plan as the organization learns from early deployments and faces new challenges and opportunities. The payoff is a more agile, data-driven enterprise in which AI initiatives are governed strategically, data assets are managed as strategic resources, and cross-functional teams collaborate effectively to deliver consistent, measurable business value. For many organizations, the CDAO-led approach is not just a change in leadership but a transformation of how data is treated, governed, and leveraged to drive enterprise AI success.

Measuring the Value of CDAO-Led AI Strategy

The deployment of a CDAO-led AI strategy must be accompanied by a rigorous set of performance measurements that quantify the business impact of data and AI initiatives. Gartner emphasizes that CDAOs will be judged not only by their technical capabilities but by their ability to translate data prowess into tangible business outcomes. This means establishing a robust framework for measuring data quality, governance effectiveness, AI model performance, and the broader business impact of analytics and AI programs. The following dimensions capture essential measures that organizations can track:

Data quality and trust. Assess improvements in data quality, accuracy, completeness, timeliness, and consistency across key data domains. Monitor metrics such as data quality scores, the frequency of data quality incidents, and the rate of data remediation. A higher data quality level correlates with more reliable analytics results and more effective AI models.
Data readiness and accessibility. Track how quickly data assets can be discovered, accessed, and prepared for analytics and AI workloads. Metrics may include data catalog usage, data preparation time, data access latency, and the number of data assets available for AI use cases. Higher readiness translates into faster time-to-value for AI initiatives and lower friction for data-driven decision-making.
AI governance and risk management. Measure the effectiveness of AI governance processes, including model validation, bias detection, explainability, and monitoring. Track the frequency of governance reviews, the number of models undergoing validation, and the rate of model performance drift detection. Strong governance reduces risk and increases trust in AI systems.
AI adoption and productization. Monitor the adoption rate of AI-enabled features across products and services, the breadth of AI usage across business units, and the extent to which analytics and AI capabilities influence decision-making. Higher adoption indicates that analytics and AI are becoming embedded in products and processes, driving value across the organization.
Business outcomes and ROI. Evaluate the tangible business impact of AI initiatives, including revenue growth, cost reduction, productivity improvements, and customer experience enhancements attributable to AI deployments. Link these outcomes to specific AI programs and data initiatives to demonstrate direct value to the business.
Time-to-value and program velocity. Track the speed with which AI projects move from ideation to deployment, as well as the cadence of AI capability expansion across the enterprise. Shorter time-to-value indicates a more agile, data-driven organization capable of delivering rapid AI-enabled improvements.
Governance continuity and auditability. Assess the robustness of governance processes, the existence of auditable records, and the organization’s ability to sustain governance as new AI use cases emerge. This includes documenting policy changes, decision rationales, and governance outcomes to support ongoing accountability.
Cultural transformation and data literacy. Measure progress in fostering a data-driven culture, including improvements in data literacy, user engagement with analytics tools, and the willingness of teams to experiment with data and AI. Cultural change is a precursor to sustainable value realization, as teams become more capable and confident in using data to inform decisions.

To maximize the value of CDAOs, organizations should align the measurement framework with their strategic priorities and governance practices. This alignment ensures that the data and AI investments are continuously evaluated against the business outcomes they are intended to achieve. The CDAO’s leadership is essential in driving this alignment, establishing clear expectations, and ensuring transparency in reporting to executives, boards, and stakeholders. The ultimate objective is to demonstrate a durable link between data governance, AI strategy, and measurable business value, reinforcing the strategic importance of data leadership in the AI-enabled enterprise.

Risks, Rewards, and the Path Forward for CDAOs

As AI investments accelerate and data strategies mature, the CFO, CEO, and board increasingly expect CDAOs to deliver measurable value through data governance and AI-enabled initiatives. Gartner’s projections underscore the high-stakes nature of the CDAO role. By 2027, it is projected that 75% of CDAOs who are not perceived as essential to the success of AI implementation will lose their C-level status within their organizations. This stark statistic underscores the critical requirement for CDAOs to demonstrate ongoing strategic relevance and tangible value. The market reality is that data and AI initiatives must be continuously aligned with business goals, and CDAOs must actively translate technical capabilities into outcomes that matter to the enterprise.

The risk of demotion is a powerful incentive for CDAOs to invest in the cross-functional collaboration required to make AI initiatives successful. It also underscores the need for CDAOs to build credibility and visibility across the organization by delivering measurable results, communicating effectively with executives and the board, and ensuring that governance and data foundations support scalable, responsible AI. The path forward requires CDAOs to be proactive about governance, data readiness, and the strategic value of AI, while cultivating the leadership presence to navigate complex organizational dynamics and political considerations.

Boards and executives, in turn, should recognize that the CDAO role is not a singular technical function but a strategic enabler of enterprise AI. They should expect CDAOs to deliver consistent, auditable governance, demonstrated ROI, and clear alignment between data strategy, AI strategy, and business strategy. The organizational design and governance model should empower CDAOs to scale AI responsibly, accelerate value realization, and sustain momentum in a rapidly evolving AI landscape. For CDAOs, the objective is to balance the need for rapid AI deployment with the necessity of robust governance, ethical standards, and risk controls. The most successful CDAOs will be those who demonstrate that their data leadership translates into concrete business outcomes—revenue opportunities unlocked by AI-enabled products, operational improvements achieved through data-driven decision-making, and enhanced customer experiences driven by AI-powered services.

The Gartner framework thus points toward a future in which CDAOs occupy a central position in enterprise leadership, wielding authority over AI strategy and data governance, and coordinating cross-functional teams to deliver sustained business value. The strategic imperative for organizations is to invest in data governance maturity, to staff and develop CDAOs with the right blend of technical depth and business acumen, and to foster a culture that treats data as a strategic asset and AI capability as a source of enduring competitive advantage. The CDAO-led AI strategy, when executed with discipline and a clear alignment to business outcomes, can accelerate digital transformation, improve decision-making, and unlock new sources of value that redefine what is possible in the AI-enabled enterprise.

The Roadmap to Enterprise AI Maturity: What Leaders Should Do Next

For organizations seeking to capitalize on the CDAOs’ emerging leadership, here are practical recommendations that reflect Gartner’s insights and best practices for building a data-driven, AI-enabled enterprise. These recommendations aim to help organizations accelerate AI maturity, strengthen governance, and drive measurable business value through data leadership at the highest level.

Recognize data as a strategic asset at the executive level. Elevate the CDAO’s role and ensure direct reporting lines to the CEO or the highest levels of leadership to reinforce the strategic importance of data governance and AI strategy. Align data and AI investments with the organization’s long-term objectives, ensuring that data governance and AI initiatives have explicit, measurable business outcomes that resonate with boards and executives.
Build a governance-first foundation. Establish a robust data governance and AI governance framework that covers data quality, data lineage, access controls, privacy, security, bias mitigation, and accountability. Create governance councils and roles with clear decision rights to ensure consistent policies across the organization. This foundation should be designed to scale with AI maturity and evolving regulatory requirements.
Invest in AI-ready data platforms and capabilities. Prioritize data architecture, data integration, metadata management, data cataloging, and data quality tooling to enable reliable AI workflows. Create standardized data models and data definitions to ensure consistent data usage across units. Align data foundations with the analytics and AI pipelines necessary to support enterprise-scale AI deployments.
Promote cross-functional collaboration and data literacy. Foster a culture of data-driven decision-making by bridging gaps between IT, data science, product development, operations, and business units. Build a community of practice that shares best practices, success stories, and lessons learned. Invest in training to raise data literacy across the organization, enabling non-technical stakeholders to understand and apply data-driven insights.
Align AI strategy with business outcomes. Ensure that AI initiatives address concrete business questions and outcomes, such as revenue growth, operational efficiency, customer experience, risk reduction, or product innovation. Use a structured portfolio approach to manage AI initiatives, with clear prioritization, milestones, and expected ROI. Communicate the business value of AI initiatives in terms that resonate with executives and business leaders.
Establish ethical and responsible AI standards. Implement ethical guidelines for AI development and deployment, including bias mitigation, fairness, transparency, accountability, and privacy protection. Integrate these standards into governance policies, model validation processes, and ongoing monitoring. This approach will help maintain public trust and regulatory compliance as AI becomes more pervasive across the organization.
Measure impact and demonstrate value. Define and track metrics that capture data quality, AI performance, adoption, time-to-value, and business outcomes. Use dashboards and reporting that translate technical performance into business impact, and provide regular updates to leadership to reinforce the strategic importance of data governance and AI strategy.
Prepare for scale and maturation. Plan for the expansion of data and AI initiatives as the organization’s AI maturity grows. Develop scalable processes, governance structures, and talent pipelines to support a broader range of AI use cases. Continuously refine the CDAO’s role to reflect evolving priorities, stakeholder needs, and regulatory environments.
Invest in leadership development and talent management. Build a pipeline of leaders who can operate effectively across data governance, analytics, and AI strategy. Provide mentorship, cross-functional training, and opportunities to work on high-impact AI programs. Cultivate a leadership culture that values data-driven decision making, collaboration, and responsible innovation.
Align incentives and accountability. Tie performance incentives to measurable AI outcomes and governance effectiveness. Ensure accountability for data quality, AI performance, and governance, with clear consequences for noncompliance and clear recognition for success. This alignment reinforces the strategic importance of the CDAO function and drives sustained attention to data and AI initiatives.
Foster an adaptive governance model. Create governance processes flexible enough to accommodate new AI use cases, regulatory changes, and evolving risk profiles. Maintain a continuous improvement mindset, with regular reviews of governance effectiveness and updates to policies, standards, and controls as needed.
Communicate progress and learnings. Build ongoing communication channels that share success stories, lessons learned, and updates on AI strategy with the entire organization. The exchange of knowledge helps sustain momentum, fosters transparency, and keeps stakeholders engaged in the AI journey.

The convergence of governance, data readiness, and AI strategy represents a powerful framework for enterprise AI. The CDAO role anchors this convergence, guiding strategic decisions, energizing cross-functional collaboration, and ensuring that data assets and AI capabilities translate into sustained business value. By following these practical steps, organizations can build a resilient, scalable, and ethical AI-enabled enterprise that leverages data as a core strategic asset and positions the CDAO as a pivotal driver of competitive advantage in the AI era.

Conclusion

The business landscape is undergoing a quiet but decisive transformation as CDAOs ascend to the forefront of AI strategy. Gartner’s findings—highlighting that 70% of CDAOs now lead AI initiatives—mark the shift from CIO- and CTO-led models to data-driven leadership. The escalation of CDAOs into direct CEO reporting lines in 2025 and the broader maturation of data governance reflect a fundamental belief: data is not simply a by-product of operations; it is the asset that unlocks AI’s value and sustains competitive advantage in a data-centric economy. As enterprise AI initiatives accelerate, the CDAO role continues to evolve, moving through distinct paths—the Expert Data and Analytics Leader, the Connector, and the Pioneer CDAx—each offering unique strengths and strategic emphases. The rising stakes, including Gartner’s warning that 75% of CDAOs not seen as essential to AI success may lose their C-level status by 2027, underscore the imperative for CDAOs to demonstrate credible, measurable business impact through robust governance, data readiness, and cross-functional collaboration.

To thrive in this environment, organizations should pursue a data-centric leadership model that integrates governance and readiness with AI execution and business outcomes. This means elevating the CDAO to a strategic position, investing in governance frameworks, building AI-ready data foundations, and fostering cross-functional collaboration across business units. It also means developing a practical roadmap that translates data and AI capabilities into tangible business value, while maintaining rigorous governance, ethical standards, and risk management. By embracing this approach, enterprises can accelerate their AI maturity, sustain momentum through governance and leadership, and realize the full potential of data-driven AI strategies in an ever-evolving digital economy. Through disciplined leadership, thoughtful organizational design, and a relentless focus on measurable outcomes, CDAOs can and will shape the future of enterprise AI, guiding organizations toward a more intelligent, resilient, and competitive horizon.